Development and investigation of a non-invasive disease severity index for inflammatory bowel disease.

INTRODUCTION: The disease severity index (DSI) encapsulates the inflammatory bowel disease (IBD) burden but requires endoscopic investigations. This study developed a non-invasive DSI using faecal calprotectin (DSI-fCal) and faecal myeloperoxidase (DSI-fMPO) instead of colonoscopy. METHODS: Adults with IBD were recruited prospectively. Baseline biomarker concentrations were used to develop DSI-fCal and DSI-fMPO, and these were correlated with the original DSI, IBD-symptoms, endoscopic activity, and quality-of-life (QoL). Area under the receiver-operating-characteristics curves (AUROC) assessed DSI-fCal/DSI-fMPO as predictors of clinical and biochemical remission at six months (symptom remission and fCal <150 µg/g, respectively), and a complicated IBD-course at 24 months (disease relapse needing escalation of biologicals/immunomodulators/recurrent corticosteroids, IBD-hospitalisations/surgeries). Multivariable logistic regression assessed the utility of DSI-fCal/DSI-fMPO in predicting a complicated IBD-course at 24 months. RESULTS: In total, 171 patients were included (Crohn's disease=99, female=90, median age=46y (IQR 36-59)). DSI-fCal and DSI-fMPO correlated with the original DSI (r>0.9, p<0.001), endoscopic indices (r=0.45-0.49, p<0.001), IBD-symptoms (r=0.53-0.58, p<0.001) and QoL (r=-0.57-0.58, p<0.001). Baseline DSI-fCal (AUROC=0.79, 95% CI 0.65-0.92) and DSI-fMPO (AUROC=0.80, 95% CI 0.67-0.93) were associated with 6-month clinical and biochemical remission. DSI-fCal (AUROC=0.83, 95% CI 0.77-0.89) and DSI-fMPO (AUROC=0.80, 95% CI 0.73-0.87) performed similarly in predicting a complicated IBD-course to the original DSI (pdifference>0.05). The non-invasive DSI was independently associated with a complicated IBD-course on multivariable analyses (DSI-fCal28, aOR=6.04, 95% CI 2.42-15.08; DSI-fMPO25, aOR=7.84, 95% CI 2.96-20.73). CONCLUSIONS: The DSI-fCal and DSI-fMPO perform similarly in prognosticating the longitudinal disease course as the original DSI, whilst avoiding a need for an endoscopic assessment.

Factors influencing smartwatch use and comfort with health data sharing: a sequential mixed-method study protocol.

INTRODUCTION: Smartwatches have become ubiquitous for tracking health metrics. These data sets hold substantial potential for enhancing healthcare and public health initiatives; it may be used to track chronic health conditions, detect previously undiagnosed health conditions and better understand public health trends. By first understanding the factors influencing one's continuous use of the device, it will be advantageous to assess factors that may influence a person's willingness to share their individual data sets. This study seeks to comprehensively understand the factors influencing the continued use of these devices and people's willingness to share the health data they generate. METHODS AND ANALYSIS: A two-section online survey of smartwatch users over the age of 18 will be conducted (n ≥200). The first section, based on the expectation-confirmation model, will assess factors influencing continued use of smartwatches while the second section will assess willingness to share the health data generated from these devices. Survey data will be analysed descriptively and based on structural equation modelling.Subsequently, six focus groups will be conducted to further understand the issues raised in the survey. Each focus group (n=6) will consist of three smartwatch users: a general practitioner, a public health specialist and an IT specialist. Young smartwatch users (aged 18-44) will be included in three of the focus groups and middle-aged smartwatch users (aged 45-64) will be included in the other three groups. This is to enhance comparison of opinions based on age groups. Data from the focus groups will be analysed using the microinterlocutor approach and an executive summary.After the focus group, participants will complete a brief survey to indicate any changes in their opinions resulting from the discussion. ETHICS AND DISSEMINATION: The results of this study will be disseminated through publication in a peer-reviewed journal, and all associated data will be deposited in a relevant, publicly accessible data repository to ensure transparency and facilitate future research endeavours.This study was approved by the Social Research Ethic Committee (SREC), University College Cork-SREC/SOM/21062023/2.

Using publicly available data to predict recreational cannabis legalization at the county-level: A machine learning approach.

BACKGROUND: There is substantial geographic variability in local cannabis policies within states that have legalized recreational cannabis. This study develops an interpretable machine learning model that uses county-level population demographics, sociopolitical factors, and estimates of substance use and mental illness prevalences to predict the legality of recreational cannabis sales within each U.S. county. METHODS: We merged data and selected 14 model inputs from the 2010 Census, 2012 County Presidential Data from the MIT Elections Lab, and Small Area Estimates from the National Surveys on Drug Use and Health (NSDUH) from 2010 to 2012 at the county level. County policies were labeled as having recreational cannabis legal (RCL) if the sale of recreational cannabis was allowed anywhere in the county in 2014, resulting in 92 RCL and 3002 non-RCL counties. We used synthetic data augmentation and minority oversampling techniques to build an ensemble of 1000 logistic regressions on random sub-samples of the data, withholding one state at a time and building models from all remaining states. Performance was evaluated by comparing the predicted policy conditions with the actual outcomes in 2014. RESULTS: When compared to the actual RCL policies in 2014, the ensemble estimated predictions of counties transitioning to RCL had a macro f1 average score of 0.61. The main factors associated with legalizing county-level recreational cannabis sales were the prevalences of past-month cannabis use and past-year cocaine use. CONCLUSION: By leveraging publicly available data from 2010 to 2012, our model was able to achieve appreciable discrimination in predicting counties with legal recreational cannabis sales in 2014, however, there is room for improvement. Having demonstrated model performance in the first handful of states to legalize cannabis, additional testing with more recent data using time to event models is warranted.

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

Nausea and vomiting of pregnancy and prenatal cannabis use in a Michigan sample.

BACKGROUND: Nausea and vomiting of pregnancy, also referred to as morning sickness, affects more than 70% of all pregnancies. Symptoms range from mild to severe and, in some cases, can be debilitating, resulting in a reduced quality of life. Moreover, prenatal cannabis use prevalence has doubled in the United States, and cannabis potency, measured by the concentration of delta-9-tetrahydrocannabiniol, has increased from 10% in 2009 to 14% in 2019. State-level recreational legalization of cannabis may contribute to the liberalization of its use and reduced risk perception. Furthermore, the relatively recent discovery of cannabinoid hyperemesis syndrome may contribute to the mischaracterization of morning sickness in individuals who use cannabis during pregnancy. Although cannabis has well-documented antiemetic properties, there is insufficient research on the topic. Therefore, it is essential to establish a tangible understanding of the association between nausea and vomiting of pregnancy and prenatal cannabis use. OBJECTIVE: This study aimed to estimate the degree to which nausea and vomiting of pregnancy might be associated with prenatal cannabis use in a sample of pregnant people in Michigan, a state where recreational cannabis use became legal in December 2018. STUDY DESIGN: This was a prospective study of participants from the Michigan Archive for Research on Child Health, a population-based pregnancy cohort that was recruited using a probability-based sampling approach. Participants were recruited from 22 prenatal clinics located throughout Michigan's lower peninsula. Cross-sectional analyses were performed for data available between October 2017 and January 2022. RESULTS: Among this sample of Michigan pregnant people, 14% (95% confidence interval, 11%-16%) reported cannabis use. Participants who experienced increasing morning sickness severity had higher odds of using cannabis (adjust odds ratio, 1.2; 95% confidence interval, 1.1-1.2). When the sample was restricted to first-trimester morning sickness and cannabis use, the results remained statistically robust. When the direction of the association was reversed, an increase in morning sickness severity was detected among participants who used cannabis during pregnancy (ßadjusted, 0.2; 95% confidence interval, 0.1-0.2). Lastly, the association between prepregnancy cannabis use and first-trimester morning sickness was investigated. Study findings suggest an increase in morning sickness severity among people who used cannabis in the 3 months before pregnancy compared with those who did not use cannabis (ßadjusted, 0.1; 95% confidence interval, 0.003-0.200). CONCLUSION: Study findings indicated a link between nausea and vomiting of pregnancy and prenatal cannabis use. Moreover, this study revealed that using cannabis in the 3 months before pregnancy is associated with first-trimester morning sickness severity. The strengths of our study contribute to the scant epidemiologic evidence in this area of research. More fine-grained, time-specific data on nausea and vomiting of pregnancy and prenatal cannabis use are necessary to draw inferences about cause-effect relationships. Our study might provide a basis to discourage cannabis use during pregnancy until more evidence is collected.

TRPA1 Antagonist LY3526318 Inhibits the Cinnamaldehyde-Evoked Dermal Blood Flow Increase: Translational Proof of Pharmacology.

Transient receptor potential Ankyrin 1 (TRPA1) is an ion channel expressed by sensory neurons, where it mediates pain signaling. Consequently, it has emerged as a promising target for novel analgesics, yet, to date, no TRPA1 antagonists have been approved for clinical use. In the present translational study, we utilized dermal blood flow changes evoked by TRPA1 agonist cinnamaldehyde as a target engagement biomarker to investigate the in vivo pharmacology of LY3526318, a novel TRPA1 antagonist. In rats, LY3526318 (1, 3, and 10 mg/kg, p.o.) dose-dependently reduced the cutaneous vasodilation typically observed following topical application of 10% v/v cinnamaldehyde. The inhibition was significant at the site of cinnamaldehyde application and also when including an adjacent area of skin. Similarly, in a cohort of 16 healthy human volunteers, LY3526318 administration (10, 30, and 100 mg, p.o.) dose-dependently reduced the elevated blood flow surrounding the site of 10% v/v cinnamaldehyde application, with a trend toward inhibition at the site of application. Comparisons between both species reveal that the effects of LY3526318 on the cinnamaldehyde-induced dermal blood flow are greater in rats relative to humans, even when adjusting for cross-species differences in potency of the compound at TRPA1. Exposure-response relationships suggest that a greater magnitude response may be observed in humans if higher antagonist concentrations could be achieved. Taken together, these results demonstrate that cinnamaldehyde-evoked changes in dermal blood flow can be utilized as a target engagement biomarker for TRPA1 activity and that LY3526318 antagonizes the ion channel both in rats and humans.

Creating, curating and evaluating a mitogenomic reference database to improve regional species identification using environmental DNA.

Species detection using eDNA is revolutionizing global capacity to monitor biodiversity. However, the lack of regional, vouchered, genomic sequence information-especially sequence information that includes intraspecific variation-creates a bottleneck for management agencies wanting to harness the complete power of eDNA to monitor taxa and implement eDNA analyses. eDNA studies depend upon regional databases of mitogenomic sequence information to evaluate the effectiveness of such data to detect and identify taxa. We created the Oregon Biodiversity Genome Project to create a database of complete, nearly error-free mitogenomic sequences for all of Oregon's fishes. We have successfully assembled the complete mitogenomes of 313 specimens of freshwater, anadromous and estuarine fishes representing 24 families, 55 genera and 129 species and lineages. Comparative analyses of these sequences illustrate that many regions of the mitogenome are taxonomically informative, that the short (~150 bp) mitochondrial 'barcode' regions typically used for eDNA assays do not consistently diagnose for species and that complete single or multiple genes of the mitogenome are preferable for identifying Oregon's fishes. This project provides a blueprint for other researchers to follow as they build regional databases, illustrates the taxonomic value and limits of complete mitogenomic sequences and offers clues as to how current eDNA assays and environmental genomics methods of the future can best leverage this information.

On the Importance of Coefficient Alpha for Measurement Research: Loading Equality Is Not Necessary for Alpha's Utility as a Scale Reliability Index.

The population relationship between coefficient alpha and scale reliability is studied in the widely used setting of unidimensional multicomponent measuring instruments. It is demonstrated that for any set of component loadings on the common factor, regardless of the extent of their inequality, the discrepancy between alpha and reliability can be arbitrarily small in any considered population and hence practically ignorable. In addition, the set of parameter values where this discrepancy is negligible is shown to possess the same dimensionality as that of the underlying model parameter space. The article contributes to the measurement and related literature by pointing out that (a) approximate or strict loading identity is not a necessary condition for the utility of alpha as a trustworthy index of scale reliability, and (b) coefficient alpha can be a dependable reliability measure with any extent of inequality in the component loadings.

Neonatal brain MRI and short-term outcomes after acute provoked seizures.

OBJECTIVE: We investigated how diagnosis and injury location on neonatal brain MRI following onset of acute provoked seizures was associated with short term outcome. STUDY DESIGN: A multicenter cohort of neonates with acute provoked seizures enrolled in the Neonatal Seizure Registry. MRIs were centrally evaluated by a neuroradiologist for location of injury and radiologic diagnosis. Clinical outcomes were determined by chart review. Multivariate logistic regression was used to examine the association between MRI findings and outcomes. RESULTS: Among 236 newborns with MRI at median age 4 days (IQR 3-8), 91% had abnormal MRI. Radiologic diagnoses of intracranial hemorrhage (OR 3.2 [1.6-6.5], p < 0.001) and hypoxic-ischemic encephalopathy (OR 2.7 [1.4-5.4], p < 0.003) were associated with high seizure burden. Radiologic signs of intracranial infection were associated with abnormal neurologic examination at discharge (OR 3.9 [1.3-11.6], p < 0.01). CONCLUSION: Findings on initial MRI can help with expectant counseling on short-term outcomes following acute provoked neonatal seizures.

COVID-19 in congenital heart disease (COaCHeD) study.

BACKGROUND: COVID-19 has caused significant worldwide morbidity and mortality. Congenital heart disease (CHD) is likely to increase vulnerability and understanding the predictors of adverse outcomes is key to optimising care. OBJECTIVE: Ascertain the impact of COVID-19 on people with CHD and define risk factors for adverse outcomes. METHODS: Multicentre UK study undertaken 1 March 2020-30 June 2021 during the COVID-19 pandemic. Data were collected on CHD diagnoses, clinical presentation and outcomes. Multivariable logistic regression with multiple imputation was performed to explore predictors of death and hospitalisation. RESULTS: There were 405 reported cases (127 paediatric/278 adult). In children (age <16 years), there were 5 (3.9%) deaths. Adjusted ORs (AORs) for hospitalisation in children were significantly lower with each ascending year of age (OR 0.85, 95% CI 0.75 to 0.96 (p<0.01)). In adults, there were 24 (8.6%) deaths (19 with comorbidities) and 74 (26.6%) hospital admissions. AORs for death in adults were significantly increased with each year of age (OR 1.05, 95% CI 1.01 to 1.10 (p<0.01)) and with pulmonary arterial hypertension (PAH; OR 5.99, 95% CI 1.34 to 26.91 (p=0.02)). AORs for hospitalisation in adults were significantly higher with each additional year of age (OR 1.03, 95% CI 1.00 to 1.05 (p=0.04)), additional comorbidities (OR 3.23, 95% CI 1.31 to 7.97 (p=0.01)) and genetic disease (OR 2.87, 95% CI 1.04 to 7.94 (p=0.04)). CONCLUSIONS: Children were at low risk of death and hospitalisation secondary to COVID-19 even with severe CHD, but hospital admission rates were higher in younger children, independent of comorbidity. In adults, higher likelihood of death was associated with increasing age and PAH, and of hospitalisation with age, comorbidities and genetic disease. An individualised approach, based on age and comorbidities, should be taken to COVID-19 management in patients with CHD.

The test-retest reliability of large and small fiber nerve excitability testing with threshold tracking.

Objective: Standard nerve excitability testing (NET) predominantly assesses Aα- and Aß-fiber function, but a method examining small afferents would be of great interest in pain studies. Here, we examined the properties of a novel perception threshold tracking (PTT) method that preferentially activates Aδ-fibers using weak currents delivered by a novel multipin electrode and compared its reliability with NET. Methods: Eighteen healthy subjects (mean age:34.06 ±â€¯2.0) were examined three times with motor and sensory NET and PTT in morning and afternoon sessions on the same day (intra-day reliability) and after a week (inter-day reliability). NET was performed on the median nerve, while PTT stimuli were delivered through a multipin electrode located on the forearm. During PTT, subjects indicated stimulus perception via a button press and the intensity of the current was automatically increased or decreased accordingly by Qtrac software. This allowed changes in the perception threshold to be tracked during strength-duration time constant (SDTC) and threshold electrotonus protocols. Results: The coefficient of variation (CoV) and interclass coefficient of variation (ICC) showed good-excellent reliability for most NET parameters. PTT showed poor reliability for both SDTC and threshold electrotonus parameters. There was a significant correlation between large (sensory NET) and small (PTT) fiber SDTC when all sessions were pooled (r = 0.29, p = 0.03). Conclusions: Threshold tracking technique can be applied directly to small fibers via a psychophysical readout, but with the current technique, the reliability is poor. Significance: Further studies are needed to examine whether Aß-fiber SDTC may be a surrogate biomarker for peripheral nociceptive signalling.

SDH deficiency is very common in carotid body paragangliomas: Genetic counseling and testing should be offered to all patients.

BACKGROUND: Collectively, germline pathogenic variants in succinate dehydrogenase (SDH) genes are the most common cause of hereditary paragangliomas. Loss of immunohistochemical expression of SDHB protein (termed SDH deficiency) occurs whenever there is biallelic inactivation of any SDH gene. We sought to estimate the prevalence of SDH deficiency in patients with carotid body paragangliomas. METHODS: We identified all carotid body paragangliomas that had undergone surgical excision at our institution over the last 30 years. If SDHB immunohistochemistry was not performed at the time of excision, it was performed on archived material. RESULTS: There were 64 carotid body paragangliomas identified in the 62 patients. Two-thirds of the patients were female, and 43 (67%) were SDH-deficient. CONCLUSION: Up to two-thirds of all carotid body paragangliomas are associated with SDH deficiency. Therefore, genetic testing and counseling should be offered to all patients with carotid body paragangliomas, regardless of age or family history.

The elephant in the room: cybersecurity in healthcare.

Cybersecurity has seen an increasing frequency and impact of cyberattacks and exposure of Protected Health Information (PHI). The uptake of an Electronic Medical Record (EMR), the exponential adoption of Internet of Things (IoT) devices, and the impact of the COVID-19 pandemic has increased the threat surface presented for cyberattack by the healthcare sector. Within healthcare generally and, more specifically, within anaesthesia and Intensive Care, there has been an explosion in wired and wireless devices used daily in the care of almost every patient-the Internet of Medical Things (IoMT); ventilators, anaesthetic machines, infusion pumps, pacing devices, organ support and a plethora of monitoring modalities. All of these devices, once connected to a hospital network, present another opportunity for a malevolent party to access the hospital systems, either to gain PHI for financial, political or other gain or to attack the systems directly to cause erroneous monitoring, altered settings of any device and even to access the EMR via this IoMT window. This exponential increase in the IoMT and the increasing wireless connectivity of anaesthesia and ICU devices as well as implantable devices presents a real and present danger to patient safety. There has, at the same time, been a chronic underfunding of cybersecurity in healthcare. This lack of cybersecurity investment has left the sector exposed, and with the monetisation of PHI, the introduction of technically unsecure IoT devices for monitoring and direct patient care, the healthcare sector is presenting itself for further devastating cyberattacks or breaches of PHI. Coupled with the immense strain that the COVID-19 pandemic has placed on healthcare and the changes in working patterns of many caregivers, this has further amplified the exposure of the sector to cyberattacks.

Weight bias and stigma in healthcare professionals: a narrative review with a Singapore lens.

Addressing weight stigma is essential to obesity management as it causes inequalities in healthcare and impacts the outcomes of health. This narrative review summarises systematic review findings about the presence of weight bias in healthcare professionals, and interventions to reduce weight bias or stigma in these professionals. Two databases (PubMed and Cumulative Index to Nursing and Allied Health Literature [CINAHL]) were searched. Seven eligible reviews were identified from 872 search results. Four reviews identified the presence of weight bias, and three investigated trials to reduce weight bias or stigma in healthcare professionals. The findings may help further research and the treatment, health and well-being of individuals with overweight or obesity in Singapore. Weight bias was prevalent among qualified and student healthcare professionals globally, and there is a lack of clear guidance for effective interventions to reduce it, particularly in Asia. Future research is essential to identify the issues and inform initiatives to reduce weight bias and stigma among healthcare professionals in Singapore.

Longitudinal associations between age 20 problematic substance use and opioid use disorder incidence at age 30 - findings from an urban cohort.

This study used data from a longitudinal prevention study in an urban cohort to examine associations between nicotine dependence, alcohol, and cannabis use disorder and disorder criteria at age 20, with opioid use disorder (OUD) incidence or criteria onset by age 30. The study sample included 1408 participants (57.5% female, 72.5% African American) drawn from two cohorts of participants in a mid-Atlantic region of the U.S. as part of a series of randomized controlled trials of elementary school-based universal prevention interventions. Lifetime cannabis use disorder (CUD), alcohol use disorder (AUD; both DSM-IV), and current nicotine dependence (Fagerstrom Test for Nicotine Dependence, FTND) assessed at age 20 were used to predict (1) DSM-IV lifetime OUD at age 30, and (2) OUD criteria between ages 20 and 30 in multivariable logistic regression models. Covariates for all analyses included sociodemographics (sex, race, and free/reduced-priced lunch status), community disadvantage, and intervention status. Nicotine dependence (FTND≥3) at age 20 predicted age 30 DSM-IV lifetime OUD (aOR = 2.37; 95% CI 1.02,5.54). The number of CUD criteria (aOR = 1.30; 95% CI 1.09,1.57) and nicotine dependence severity scores (aOR = 1.22; 95% CI = 1.05,1.41) at age 20 predicted any OUD criteria between the ages of 20 and 30. Findings are consistent with previous research on opioid use behavior in young adulthood and suggest that nicotine dependence and CUD criteria among urban young people predict onset of OUD and OUD criteria in young adulthood.

Weight bias and stigma in healthcare professionals: a narrative review with a Singapore lens

Addressing weight stigma is essential to obesity management as it causes inequalities in healthcare and impacts the outcomes of health. This narrative review summarises systematic review findings about the presence of weight bias in healthcare professionals, and interventions to reduce weight bias or stigma in these professionals. Two databases (PubMed and Cumulative Index to Nursing and Allied Health Literature [CINAHL]) were searched. Seven eligible reviews were identified from 872 search results. Four reviews identified the presence of weight bias, and three investigated trials to reduce weight bias or stigma in healthcare professionals. The findings may help further research and the treatment, health and well-being of individuals with overweight or obesity in Singapore. Weight bias was prevalent among qualified and student healthcare professionals globally, and there is a lack of clear guidance for effective interventions to reduce it, particularly in Asia. Future research is essential to identify the issues and inform initiatives to reduce weight bias and stigma among healthcare professionals in Singapore.

Casting CRISPR-Cas13d to fish for microprotein functions in animal development.

Protein coding genes were originally identified with sequence-based definitions that included a 100-codon cutoff to avoid annotating irrelevant open reading frames. However, many active proteins contain less than 100 amino acids. Indeed, functional genetics, ribosome profiling, and proteomic profiling have identified many short, translated open reading frames, including those with biologically active peptide products (microproteins). Yet, functions for most of these peptide products remain unknown. Because microproteins often act as key signals or fine-tune processes, animal development has already revealed functions for a handful of microproteins and provides an ideal context to uncover additional microprotein functions. However, many mRNAs during early development are maternally provided and hinder targeted mutagenesis approaches to characterize developmental microprotein functions. The recently established, RNA-targeting CRISPR-Cas13d system in zebrafish overcomes this barrier and produces potent knockdown of targeted mRNA, including maternally provided mRNA, and enables flexible, efficient interrogation of microprotein functions in animal development.

Radiology and Te Whatu Ora - Health New Zealand in 2022. Why we should all care.

Radiology is a key enabler of clinical activity and has been shown to be highly cost effective. Demand and activity have increased over time, with demand for computed tomography (CT), magnetic resource imaging (MRI) and ultrasound (US) growing faster than population growth. Complexity has also increased over time. Resources in the public sector have not kept up with demand, exacerbated by the COVID-19 pandemic. A reliance on an overseas trained workforce has resulted in critical shortages. Waiting times for CT, MRI and US across Aotearoa New Zealand remain well below targets and have not improved over 10 years. Robust links between clinical activity and radiology resourcing are needed to address the deficits and thereby maintain clinical safety.

Estimating the effects of legalizing recreational cannabis on newly incident cannabis use.

Liberalized state-level recreational cannabis policies in the United States (US) fostered important policy evaluations with a focus on epidemiological parameters such as proportions [e.g., active cannabis use prevalence; cannabis use disorder (CUD) prevalence]. This cannabis policy evaluation project adds novel evidence on a neglected parameter-namely, estimated occurrence of newly incident cannabis use for underage (<21 years) versus older adults. The project's study populations were specified to yield nationally representative estimates for all 51 major US jurisdictions, with probability sample totals of 819,543 non-institutionalized US civilian residents between 2008 and 2019. Standardized items to measure cannabis onsets are from audio computer-assisted self-interviews. Policy effect estimates are from event study difference-in-difference (DiD) models that allow for causal inference when policy implementation is staggered. The evidence indicates no policy-associated changes in the occurrence of newly incident cannabis onsets for underage persons, but an increased occurrence of newly onset cannabis use among older adults (i.e., >21 years). We offer a tentative conclusion of public health importance: Legalized cannabis retail sales might be followed by the increased occurrence of cannabis onsets for older adults, but not for underage persons who cannot buy cannabis products in a retail outlet. Cannabis policy research does not yet qualify as a mature science. We argue that modeling newly incident cannabis use might be more informative than the modeling of prevalences when evaluating policy effects and provide evidence of the advantages of the event study model over regression methods that seek to adjust for confounding factors.